Simulating extreme cold winters in France with empirical importance sampling

Extreme winter cold temperatures in Europe have huge societal impacts. Being able to simulate worst-case scenarios of such events for present and future climates is hence crucial for adaptation. Rare event algorithms have been applied to simulate extreme heat waves. They have emphasized the role of atmospheric circulation in such extremes. The goal of this study is to test such algorithms for extreme cold spells.

We focus first on winter cold temperatures that have occurred in France from 1950 to 2021 and then on winter cold spells that could occur in the future according to different emissions pathways. We investigate winter mean temperatures in France (December, January, and February) and identify a record-shattering event in 1963. We find that, although the frequency of extreme cold spells decreases with time, their intensity is stationary.

We applied a stochastic weather generator approach with importance sampling, to simulate the coldest winters that could occur every year since 1950. We hence simulated ensembles of worst winter cold spells that are consistent with observations. Only some of the simulations reach colder temperatures than the record-shattering event of 1963, and the ensembles do not yield the trend that is observed in the mean temperature. The atmospheric circulation that prevails during those events is analyzed and compared to the observed circulation during the record-breaking events.